44 Data Privacy and Academic Integrity
Respecting student autonomy by seeking their consent before submitting their work to AI systems is fundamental to academic integrity. Allowing students to make informed decisions about how their work is used fosters a sense of ownership and responsibility. It also educates students about intellectual property rights, privacy issues, and the ethical use of technology, reinforcing the principles of academic integrity.
Submitting a student’s work to AI systems has significant implications for academic integrity, impacting various aspects of the educational process. One major concern is the use of AI for plagiarism detection. While these systems help maintain academic standards by identifying unoriginal work, they can create ethical issues if student submissions are added to the database without their consent. This can lead to future students’ work being unfairly flagged for plagiarism. Additionally, AI systems might not always accurately distinguish between legitimate citations and actual plagiarism, potentially resulting in false accusations and compromising fairness and due process.
Unauthorized use of student work is another critical issue. When instructors submit student work to AI systems without obtaining explicit consent, it violates the students’ intellectual property rights and erodes trust in the educational system. Respecting students’ original contributions is a cornerstone of academic integrity, and unauthorized use of their work undermines this principle. Moreover, transparency and trust between students and educators are vital. If instructors use AI systems without informing students, it can erode this trust, creating an environment of suspicion and reducing students’ willingness to engage openly with their coursework.
The fairness of academic assessments can also be impacted by the use of AI systems. These systems might introduce biases or errors in evaluating student work, affecting the fairness of grades and academic standing. For example, AI systems can perpetuate biases present in their training data, leading to unfair evaluations. Additionally, reliance on AI for grading or feedback might lead to inconsistencies compared to human evaluation, which can unfairly affect students’ grades and academic standing.
Maintaining the confidentiality of student submissions is crucial for academic integrity. When student work is submitted to AI systems, particularly external ones, it is at risk of data breaches and unauthorized access. Poor security measures in AI systems can expose student work to potential misuse, and instructors might unknowingly violate institutional policies on data privacy and confidentiality. This can lead to disciplinary actions and undermine the institution’s commitment to protecting student rights.
This work was generated in collaboration with ChatGPT, an AI language model developed by OpenAI.
This work is licensed under CC BY 4.0